Update blurpool.py

clean up code for PR

timm/models/layers/blurpool.py

@@ -1,7 +1,7 @@
-'''independent attempt to implement
-
-MaxBlurPool2d in a more general fashion(separate maxpooling from BlurPool)
-which was again inspired by
+'''
+BlurPool layer inspired by
+Kornia's Max_BlurPool2d
+and
 Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
 
 '''
@@ -17,8 +17,7 @@ class BlurPool2d(nn.Module):
     Corresponds to the Downsample class, which does blurring and subsampling
     Args:
         channels = Number of input channels
-        blur_filter_size (int): filter size for blurring. currently supports either 3 or 5 (most common)
-                                defaults to 3.
+        blur_filter_size (int): binomial filter size for blurring. currently supports 3(default) and 5.
         stride (int): downsampling filter stride
     Shape:
     Returns:
@@ -35,16 +34,16 @@ class BlurPool2d(nn.Module):
 
         if blur_filter_size == 3:
             pad_size = [1] * 4
-            blur_matrix = torch.Tensor([[1., 2., 1]]) / 4 # binomial kernel b2
+            blur_matrix = torch.Tensor([[1., 2., 1]]) / 4  # binomial filter b2
         else:
             pad_size = [2] * 4
-            blur_matrix = torch.Tensor([[1., 4., 6., 4., 1.]]) / 16 # binomial filter kernel b4
+            blur_matrix = torch.Tensor([[1., 4., 6., 4., 1.]]) / 16  # binomial filter b4
 
         self.padding = nn.ReflectionPad2d(pad_size)
         blur_filter = blur_matrix * blur_matrix.T
         self.register_buffer('blur_filter', blur_filter[None, None, :, :].repeat((self.channels, 1, 1, 1)))
 
-    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: # type: ignore
+    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:  # type: ignore
         if not torch.is_tensor(input_tensor):
             raise TypeError("Input input type is not a torch.Tensor. Got {}"
                             .format(type(input_tensor)))
@@ -53,16 +52,3 @@ class BlurPool2d(nn.Module):
                              .format(input_tensor.shape))
         # apply blur_filter on input
         return F.conv2d(self.padding(input_tensor), self.blur_filter, stride=self.stride, groups=input_tensor.shape[1])
-
-
-######################
-# functional interface
-######################
-
-
-'''def blur_pool2d() -> torch.Tensor:
-    r"""Creates a module that computes pools and blurs and downsample a given
-    feature map.
-    See :class:`~kornia.contrib.MaxBlurPool2d` for details.
-    """
-    return BlurPool2d(kernel_size, ceil_mode)(input)'''